Sarikprueck, Piampoom

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Sarikprueck, Piampoom
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piampoom.sa@kmitl.ac.th

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    Developing important renewable energies in Thailand
    (2011-12-09)
    Sarikprueck, Piampoom
    ;
    Korkua, Suratsavadee K.
    ;
    Lee, Wei Jen
    ;
    Lumyong, Pichit
    In order to enhance energy security while reducing environmental impact, the government of Thailand has established policy to develop domestic energy resources to increase energy stability and to sufficiently meet the future demand. Aiming to achieve a 20% share of renewable energy by 2022 according to the 15-Year Renewable Energy Development Plan (REDP), it is necessary to work out a plan to explore new renewable energy resources and technologies, encourage more investments from both public and private sectors, and promote entrepreneurships to transfer laboratory work to commercial production in manufacturing scale. In this paper, first the situation of Thailand's energy is presented. The energy plan in next 15 years will also be mentioned. Next, the potential of renewable energy resources are also discussed. Finally, the study of the technology roadmap of the renewable energy industry in Thailand is proposed to emphasize the importance of the present and upcoming technology and product trend. © 2011 IEEE.
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    A linear program for system level control of regional PHEV charging stations
    (2015-12-14)
    Kulvanitchaiyanunt, Asama
    ;
    Chen, Victoria C.P.
    ;
    Rosenberger, Jay
    ;
    Sarikprueck, Piampoom
    ;
    Lee, Wei Jen
    This research studies dynamic control of a system of plug-in hybrid electric vehicle (PHEV) charging stations. A finite horizon dynamic problem is presented. Based upon the 15-minute updated period of the electricity market price, the objective function is to maximize profit, which is the revenue benefit from selling back to the grid and the charging of the vehicles minus the cost of buying electricity from the grid. The state variables in each 15-minute time period consist of the total wind purchased from the system, solar power generation at each charging station, total demand at each station, and nodal market price at stations' location. This mean value problem is formulated as a deterministic linear program and solved. Potential strategies are presented to provide insight into the behavior of the system.
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    A Linear Program for System-Level Control of Regional PHEV Charging Stations
    (2016-05-01)
    Kulvanitchaiyanunt, Asama
    ;
    Chen, Victoria C.P.
    ;
    Rosenberger, Jay
    ;
    Sarikprueck, Piampoom
    ;
    Lee, Wei Jen
    This paper studies dynamic control of a system of plug-in hybrid electric vehicle (PHEV) charging stations. A finite horizon stochastic program is presented. Based upon the 15-min updated period of the electricity market price, the objective function is to maximize profit, which is the revenue benefit from selling back to the grid and the charging of the vehicles minus the cost of buying electricity from the grid. The state variables in each 15-min time period consist of the total wind purchased from the system, solar power generation at each charging station, total demand at each station, and nodal market price at stations' location. A stochastic program is formulated, and the mean value problem as a deterministic linear program is solved. Potential strategies are presented to provide insight into the behavior of the system.
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    Bounds for optimal control of a regional plug-in electric vehicle charging station system
    (2017-06-08)
    Sarikprueck, Piampoom
    ;
    Lee, Wei Jen
    ;
    Kulvanitchaiyanunt, Asama
    ;
    Chen, Victoria C.P.
    ;
    Rosenberger, Jay
    In order to support the increasing penetration of plug-in electric vehicle (PEV) users, a novel regional PEV charging station system with DC level 3 fast charging is proposed in this paper. To promote sustainable energy, the proposed system is designed to be equipped with a distributed energy storage system charged by wind generation, solar PV generation, and electricity from the power grid, which can simultaneously charge multiple PEVs. The objective of the proposed system is to minimize operational cost. Wind/solar PV generation and electricity market price are input state variables in this problem and are predicted by support vector regression (SVR). The uncertainties of the SVR models are analyzed using a Martingale Model Forecast Evolution (MMFE). Finally, bounds of the optimal operational cost in this problem are evaluated with two stochastic measures, which can be solved using the expected value problem and the wait-and-see solution. Bounds from experiments simulating models of the Dallas-Fort Worth metroplex show that the largest uncertainty in the system occurs during weekdays in the summer.
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    Medium-term operation for an industrial customer considering demand side management and risk management
    (2015-01-01)
    Ding, Zhaohao
    ;
    Sarikprueck, Piampoom
    ;
    Lee, Wei Jen
    Under a deregulated market environment, industrial customers can participate in multiple markets with different time range to purchase electricity. Transactions in different markets make the industrial customer involve in different level of cost uncertainties and risks. To solve this energy procurement portfolio problem, a medium-term operation model is proposed. The risk-term is measured and managed by mean-variance approach. The uncertainties in the proposed model are characterized by stochastic day-ahead and real-time prices generated based on ERCOT historical data. A sample case study is provided to illustrate and verify the proposed model.
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    Medium-term operation for an industrial customer considering demand side management and risk management
    (2015-09-14)
    Ding, Zhaohao
    ;
    Sarikprueck, Piampoom
    ;
    Lee, Wei Jen
    Under a deregulated market environment, industrial customers can participate in multiple markets with different time range to purchase electricity. Transactions in different markets make the industrial customer involve in different level of cost uncertainties and risks. To solve this energy procurement portfolio problem, a medium-term operation model is proposed. The risk-term is measured and managed by mean-variance approach. The uncertainties in the proposed model are characterized by stochastic day-ahead and real-time prices generated based on ERCOT historical data. A sample case study is provided to illustrate and verify the proposed model.
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    Bounds for Optimal Control of a Regional Plug-in Electric Vehicle Charging Station System
    (2018-03-01)
    Sarikprueck, Piampoom
    ;
    Lee, Wei Jen
    ;
    Kulvanitchaiyanunt, Asama
    ;
    Chen, Victoria C.P.
    ;
    Rosenberger, Jay M.
    In order to support the increasing penetration of plug-in electric vehicle (PEV) users, a novel regional PEV charging station system with dc level 3 fast charging is proposed in this paper. To promote sustainable development, the proposed system is designed to be equipped with a distributed energy storage system charged by wind generation, solar photovoltaic (PV) generation, and electricity from the power grid, which can simultaneously charge multiple PEVs. The objective of the proposed system is to minimize operational cost. Wind/solar PV generation and electricity market price are input state variables in this problem, and are predicted by support vector regression (SVR). The uncertainties of the SVR models are analyzed using a martingale model forecast evolution. Finally, bounds of the optimal operational cost in this problem are evaluated with two stochastic measures, which can be solved using the expected value problem and the wait-and-see solution. Bounds from experiments simulating models of the Dallas-Fort Worth metroplex show that the largest uncertainty in the system occurs during weekdays in the summer.